Analytics Lead
Accountable for a team's metric definitions and for the quality of others' analytical and BI work, without managing people.
4tasks in this job
73skills it uses
9tools named
The skills this job leans on most
- 1Establishing and declaring data grainNeeded in 3 of 4 tasks
- 2Governing shared data definitionsNeeded in 3 of 4 tasks
- 3Calculating and verifying KPIs against their specificationNeeded in 2 of 4 tasks
- 4Specifying BI measure semantics: additivity, aggregation and timeNeeded in 2 of 4 tasks
- 5Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 1 of 4 tasks
ToolsdbtMicrosoft Power BICollibraSQL (query language)Microsoft ExcelPythonGit / GitHubTableauQlik Sense
All 4 tasks, where people get stuck and all 73 skills
Tasks
- Define and govern a KPI metric contract
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Sampling and reviewing a team's work against standards
- Verifying reports and dashboards before and after release
- Building decision-focused reports and dashboards
All skills
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Establishing and declaring data grain
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Comparing groups
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and prioritising critical data elements
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Reviewing human- and AI-generated code changes for correctness and risk
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- Modules
- 5 open
- Practice data
- Raket Parts
6tasks in this job
87skills it uses
11tools named
The skills this job leans on most
- 1Sampling and reviewing a team's work against standardsNeeded in 6 of 6 tasks
- 2Establishing and declaring data grainNeeded in 5 of 6 tasks
- 3Governing shared data definitionsNeeded in 5 of 6 tasks
- 4Calculating and verifying KPIs against their specificationNeeded in 4 of 6 tasks
- 5Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 2 of 6 tasks
ToolsdbtCollibraMicrosoft Power BISAP S/4HANASQL (query language)Python+ 5 more
All 6 tasks, where people get stuck and all 87 skills
Tasks
- Define and govern a KPI metric contract
- Investigate inconsistent BI reporting
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Design and build a tested, governed analytical data model
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Giving specific developmental feedback on data and AI work
- Writing testable work standards and review checklists
All skills
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Establishing and declaring data grain
- Governing shared data definitions
- Sampling and reviewing a team's work against standards
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Comparing groups
- Comparing periods consistently
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining how business events flow into accounting records
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and prioritising critical data elements
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Reviewing human- and AI-generated code changes for correctness and risk
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- dbt
- Collibra
- Microsoft Power BI
- SAP S/4HANA
- SQL (query language)
- Python
- Microsoft Excel
- Microsoft PowerPoint
- Tableau
- Qlik Sense
- Git / GitHub
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- Modules
- 5 open
- Practice data
- Raket Parts
7tasks in this job
122skills it uses
12tools named
The skills this job leans on most
- 1Sampling and reviewing a team's work against standardsNeeded in 7 of 7 tasks
- 2Establishing and declaring data grainNeeded in 4 of 7 tasks
- 3Governing shared data definitionsNeeded in 4 of 7 tasks
- 4Calculating and verifying KPIs against their specificationNeeded in 3 of 7 tasks
- 5Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 2 of 7 tasks
ToolsdbtCollibraGit / GitHubSAP S/4HANASQL (query language)Python+ 6 more
All 7 tasks, where people get stuck and all 122 skills
Tasks
- Define and govern a KPI metric contract
- Investigate inconsistent BI reporting
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Analyse compensation equity with controlled disclosure and answer individual pay transparency requests
- Model the workforce plan and organisation design options with cost and people impact
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Giving specific developmental feedback on data and AI work
- Writing testable work standards and review checklists
All skills
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Establishing and declaring data grain
- Governing shared data definitions
- Sampling and reviewing a team's work against standards
- Analysing event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- Analysing pay equity with raw and adjusted pay gaps
- Analysing staged funnels: conversion, time in stage and slippage
- Answering individual pay transparency requests
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining how business events flow into accounting records
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Finding relevant data sources
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting policy scope and delegated authority limits
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Modelling workforce demand and supply
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Reviewing human- and AI-generated code changes for correctness and risk
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- dbt
- Collibra
- Git / GitHub
- SAP S/4HANA
- SQL (query language)
- Python
- Microsoft Power BI
- Tableau
- Qlik Sense
- Microsoft Excel
- Workday
- Microsoft PowerPoint
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Accountable for a team's metric definitions and for the quality of others' analytical and BI work, without managing people. Sets reporting and analysis standards in any domain, including people analytics where sensitive findings need controlled disclosure. Differs from the Analytics Manager, who owns people, priorities and delivery.
Also called Lead BI Developer, Lead Power BI Developer, Lead People Analyst, People Analytics Lead, Lead Data Analyst